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title:Python Scatter Plots | plotly

v4upgrade:true

Plotly express functions take as argument a tidy[pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html). With``px.scatter``, each data point is represented as a marker point, which location is given by the`x` and`y` columns.

importplotly_expressas px

importplotly.expressas px

iris= px.data.iris()

fig= px.scatter(iris,x="sepal_width",y="sepal_length")

fig.show()

Note that`color` and`info` data are added to hover information. You can add other columns to hover data with the`hover_data` argument of`px.scatter`.

Note that`color` and`size` data are added to hover information. You can add other columns to hover data with the`hover_data` argument of`px.scatter`.

importplotly_expressas px

importplotly.expressas px

iris= px.data.iris()

fig= px.scatter(iris,x="sepal_width",y="sepal_length",color="species",

size='petal_length')

size='petal_length',hover_data=['petal_width'])

fig.show()

importplotly_expressas px

importplotly.expressas px

gapminder= px.data.gapminder().query("continent == 'Oceania'")

px.line(gapminder,x='year',y='lifeExp',color='country')

fig= px.line(gapminder,x='year',y='lifeExp',color='country')

fig.show()

When data are not available as tidy dataframes, it is possible to use the more generic`go.Scatter` function from`plotly.graph_objs`. Whereas`plotly_express` has two functions`scatter` and`line`,`go.Scatter` can be used both for plotting points (makers) or lines, depending on the value of`mode`. The different options of`go.Scatter` are documented in its[reference page](https://plot.ly/python/reference/#scatter).

When data are not available as tidy dataframes, it is possible to use the more generic`go.Scatter` function from`plotly.graph_objects`. Whereas`plotly.express` has two functions`scatter` and`line`,`go.Scatter` can be used both for plotting points (makers) or lines, depending on the value of`mode`. The different options of`go.Scatter` are documented in its[reference page](https://plot.ly/python/reference/#scatter).

import plotly.graph_objsas go

import plotly.graph_objectsas go

import numpyas np

N=1000

random_x= np.random.randn(N)

random_y= np.random.randn(N)

t= np.linspace(0,10,100)

y= np.sin(t)

trace= go.Scatter(x=random_x,y=random_y,

mode='markers')

fig= go.Figure(data=go.Scatter(x=t,y=y,mode='markers'))

fig= go.Figure(data=[trace])

fig.show()

Use`mode` argument to choose between markers, lines, or a combination of both. For more options about line plots, see also the[line charts notebook](https://plot.ly/python/line-charts/) and the[filled area plots notebook](https://plot.ly/python/filled-area-plots/).

import plotly.graph_objsas go

import plotly.graph_objectsas go

import numpyas np

random_y1= np.random.randn(N)

random_y2= np.random.randn(N)-5

trace0= go.Scatter(x=random_x,y=random_y0,

fig= go.Figure()

fig.add_trace(go.Scatter(x=random_x,y=random_y0,

mode='markers',

name='markers')

trace1=go.Scatter(x=random_x,y=random_y1,

name='markers'))

fig.add_trace(go.Scatter(x=random_x,y=random_y1,

mode='lines+markers',

name='lines+markers')

trace2=go.Scatter(x=random_x,y=random_y2,

name='lines+markers'))

fig.add_trace(go.Scatter(x=random_x,y=random_y2,

mode='lines',

name='lines')

name='lines'))

fig= go.Figure(data=[trace0, trace1, trace2])

fig.show()

In[bubble charts](https://en.wikipedia.org/wiki/Bubble_chart), a third dimension of the data is shown through the size of markers. For more examples, see the[bubble chart notebook](https://plot.ly/python/bubble-charts/)

import plotly.graph_objsas go

import plotly.graph_objectsas go

trace0= go.Scatter(

fig=go.Figure(data=go.Scatter(

x=[1,2,3,4],

y=[10,11,12,13],

mode='markers',

marker=dict(size=[40,60,80,100],

color=[0,1,2,3])

)

))

fig= go.Figure(data=[trace0])

fig.show()

import plotly.graph_objsas go

import plotly.graph_objectsas go

import numpyas np

N=500

trace0= go.Scatter(

x= np.random.randn(N),y= np.random.randn(N)+2,

name='Above',

mode='markers',

marker=dict(

size=10,

color='rgba(152, 0, 0, .8)',

line=dict(width=2,color='rgb(0, 0, 0)')

)

)

t= np.linspace(0,10,100)

fig= go.Figure()

trace1= go.Scatter(

x=np.random.randn(N),

y=np.random.randn(N)-2,

name='Below',

fig.add_trace(go.Scatter(

x=t,y=np.sin(t),

name='sin',

mode='markers',

marker=dict(size=10,color='rgba(255, 182, 193, .9)',line_width=2)

)

))

fig.add_trace(go.Scatter(

x=t,y=np.cos(t),

name='cos',

))

fig.update_traces(mode='markers',marker_line_width=2,marker_size=10)

fig.update_layout(title='Styled Scatter',

yaxis_zeroline=False,xaxis_zeroline=False)

layout= go.Layout(title='Styled Scatter',

yaxis_zeroline=False,xaxis_zeroline=False)

fig= go.Figure(data=[trace0, trace1],layout=layout)

fig.show()

import plotly.graph_objsas go

import random

import numpyas np

import plotly.graph_objectsas go

import pandasas pd

l= []

y= []

data= pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv")

N=53

c= ['hsl('+str(h)+',50%'+',50%)'for hin np.linspace(0,360, N)]

for iinrange(int(N)):

y.append((2000+i))

trace0= go.Scatter(

x=data['Rank'],

y=data['Population']+(i*1000000),

mode='markers',

marker=dict(size=14,line_width=1,color=c[i],opacity=0.3),

name=y[i],

text=data['State'])# The hover text goes here...

l.append(trace0);

layout= go.Layout(

title='Stats of USA States',

hovermode='closest',

xaxis=dict(title='Population',zeroline=False,gridwidth=2),

yaxis=dict(title='Rank',gridwidth=2),

)

fig= go.Figure(data=l,layout=layout)

fig= go.Figure(data=go.Scatter(x=data['Postal'],

y=data['Population'],

mode='markers',

marker_color=data['Population'],

text=data['State']))# hover text goes here

fig.update_layout(title='Population of USA States')

fig.show()

import plotly.graph_objsas go

import plotly.graph_objectsas go

import numpyas np

trace1= go.Scatter(

fig=go.Figure(data=go.Scatter(

y= np.random.randn(500),

mode='markers',

marker=dict(

colorscale='Viridis',# one of plotly colorscales

)

)

))

fig.show()

for increased speed, improved interactivity, and the ability to plot even more data!

import plotly.graph_objsas go

import plotly.graph_objectsas go

import numpyas np

N=100000

trace= go.Scattergl(

fig=go.Figure(data=go.Scattergl(

x= np.random.randn(N),

y= np.random.randn(N),

mode='markers',

colorscale='Viridis',

)

)

fig= go.Figure(data=[trace])

))

fig.show()

import plotly.graph_objectsas go

import numpyas np

N=100000

r= np.random.uniform(0,1, N)

theta= np.random.uniform(0,2*np.pi, N)

fig= go.Figure(data=go.Scattergl(

x= r* np.cos(theta),# non-uniform distribution

y= r* np.sin(theta),# zoom to see more points at the center

mode='markers',

marker=dict(

color=np.random.randn(N),

colorscale='Viridis',

)

))

fig.show()